{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d8f48981",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b17d10f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "column_names = [\"id\",\"name\",\"age\",\"gender\",\"clazz\"]\n",
    "stu = pd.read_csv(\"./data/students.txt\",names=column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e6c968ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>clazz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1500100001</td>\n",
       "      <td>施笑槐</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>文科六班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500100002</td>\n",
       "      <td>吕金鹏</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>文科六班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1500100003</td>\n",
       "      <td>单乐蕊</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>理科六班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1500100004</td>\n",
       "      <td>葛德曜</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>理科三班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1500100005</td>\n",
       "      <td>宣谷芹</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>理科五班</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id name  age gender clazz\n",
       "0  1500100001  施笑槐   22      女  文科六班\n",
       "1  1500100002  吕金鹏   24      男  文科六班\n",
       "2  1500100003  单乐蕊   22      女  理科六班\n",
       "3  1500100004  葛德曜   24      男  理科三班\n",
       "4  1500100005  宣谷芹   22      女  理科五班"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stu.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d5daae25",
   "metadata": {},
   "outputs": [],
   "source": [
    "column_names = [\"id\",\"subject_id\",\"score\"]\n",
    "score = pd.read_csv(\"./data/score.txt\",names=column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0fa0798a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>subject_id</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1500100001</td>\n",
       "      <td>1000001</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500100001</td>\n",
       "      <td>1000002</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1500100001</td>\n",
       "      <td>1000003</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1500100001</td>\n",
       "      <td>1000004</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1500100001</td>\n",
       "      <td>1000005</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  subject_id  score\n",
       "0  1500100001     1000001     98\n",
       "1  1500100001     1000002      5\n",
       "2  1500100001     1000003    137\n",
       "3  1500100001     1000004     29\n",
       "4  1500100001     1000005     85"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bd3bd12c",
   "metadata": {},
   "outputs": [],
   "source": [
    "stu_score = pd.merge(stu,score,on='id',how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3b77eea5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>clazz</th>\n",
       "      <th>subject_id</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1500100001</td>\n",
       "      <td>施笑槐</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>文科六班</td>\n",
       "      <td>1000001</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500100001</td>\n",
       "      <td>施笑槐</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>文科六班</td>\n",
       "      <td>1000002</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1500100001</td>\n",
       "      <td>施笑槐</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>文科六班</td>\n",
       "      <td>1000003</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1500100001</td>\n",
       "      <td>施笑槐</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>文科六班</td>\n",
       "      <td>1000004</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1500100001</td>\n",
       "      <td>施笑槐</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>文科六班</td>\n",
       "      <td>1000005</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id name  age gender clazz  subject_id  score\n",
       "0  1500100001  施笑槐   22      女  文科六班     1000001     98\n",
       "1  1500100001  施笑槐   22      女  文科六班     1000002      5\n",
       "2  1500100001  施笑槐   22      女  文科六班     1000003    137\n",
       "3  1500100001  施笑槐   22      女  文科六班     1000004     29\n",
       "4  1500100001  施笑槐   22      女  文科六班     1000005     85"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stu_score.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fe9d0ec7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 每个班级的平均分\n",
    "mean_score = stu_score.groupby(\"clazz\")[\"score\"].mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9370f04b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clazz</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>文科一班</td>\n",
       "      <td>62.990741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>文科三班</td>\n",
       "      <td>63.945035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>文科二班</td>\n",
       "      <td>61.482759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>文科五班</td>\n",
       "      <td>60.900794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>文科六班</td>\n",
       "      <td>61.706731</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  clazz      score\n",
       "0  文科一班  62.990741\n",
       "1  文科三班  63.945035\n",
       "2  文科二班  61.482759\n",
       "3  文科五班  60.900794\n",
       "4  文科六班  61.706731"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_score.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a62d64ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "73ddf3c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.font_manager import FontProperties\n",
    "myfont=FontProperties(fname=r'C:\\Windows\\Fonts\\simhei.ttf',size=14)\n",
    "sns.set(font=myfont.get_name())\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "55e535b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='clazz', ylabel='score'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 10))\n",
    "sns.barplot(x=\"clazz\", y=\"score\", data=mean_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5c1df354",
   "metadata": {},
   "outputs": [],
   "source": [
    "tips = sns.load_dataset(\"tips\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8d9cd0f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a2e14264",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\python3.7.9\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='total_bill', ylabel='tip'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(tips[\"total_bill\"],tips['tip'], n_levels=30, cmap=\"Purples_d\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c900fee",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
